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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Using Logical Specifications For Multi-Objective Reinforcement Learning, Kolby Nottingham
Undergraduate Honors Theses
In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of environment objectives is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, we show that behaviors can be successfully specified and learned by much more expressive non-linear logical specifications. We test our agent in several environments with various objectives and show that it can generalize to many never-before-seen specifications.
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Machine Learning For Effective Parkinson's Disease Diagnosis, Brennon Brimhall
Undergraduate Honors Theses
Parkinson’s Disease is a degenerative neurological condition that affects approximately 10 million people globally. Because there is currently no cure, there is a strong motivation for research into improved and automated diagnostic procedures. Using Random Forests, a computer can effectively learn to diagnose Parkinson’s disease in a patient with high accuracy (94%), precision (95%), and recall (91%) across the data of over 2800 patients. Using similar techniques, I further determine that the most predictive medical tests relate to tremors observed in patients.